# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import importlib
import importlib.metadata
import os
import sys
import warnings
from functools import lru_cache, wraps

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env, patch_environment, str_to_bool
from .versions import compare_versions, is_torch_version


# Try to run Torch native job in an environment with TorchXLA installed by setting this value to 0.
USE_TORCH_XLA = parse_flag_from_env("USE_TORCH_XLA", default=True)

_torch_xla_available = False
if USE_TORCH_XLA:
    try:
        import torch_xla.core.xla_model as xm  # noqa: F401
        import torch_xla.runtime

        _torch_xla_available = True
    except ImportError:
        pass

# Keep it for is_tpu_available. It will be removed along with is_tpu_available.
_tpu_available = _torch_xla_available

# Cache this result has it's a C FFI call which can be pretty time-consuming
_torch_distributed_available = torch.distributed.is_available()


def _is_package_available(pkg_name, metadata_name=None):
    # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
    package_exists = importlib.util.find_spec(pkg_name) is not None
    if package_exists:
        try:
            # Some libraries have different names in the metadata
            _ = importlib.metadata.metadata(pkg_name if metadata_name is None else metadata_name)
            return True
        except importlib.metadata.PackageNotFoundError:
            return False


def is_torch_distributed_available() -> bool:
    return _torch_distributed_available


def is_xccl_available():
    if is_torch_version(">=", "2.7.0"):
        return torch.distributed.distributed_c10d.is_xccl_available()
    if is_ipex_available():
        return False
    return False


def is_ccl_available():
    try:
        pass
    except ImportError:
        print(
            "Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) XPUs, but it is not"
            " detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"
            " Bindings for PyTorch*."
        )
    return importlib.util.find_spec("oneccl_bindings_for_pytorch") is not None


def get_ccl_version():
    return importlib.metadata.version("oneccl_bind_pt")


def is_import_timer_available():
    return _is_package_available("import_timer")


def is_pynvml_available():
    return _is_package_available("pynvml") or _is_package_available("pynvml", "nvidia-ml-py")


def is_pytest_available():
    return _is_package_available("pytest")


def is_msamp_available():
    return _is_package_available("msamp", "ms-amp")


def is_schedulefree_available():
    return _is_package_available("schedulefree")


def is_transformer_engine_available():
    if is_hpu_available():
        return _is_package_available("intel_transformer_engine", "intel-transformer-engine")
    else:
        return _is_package_available("transformer_engine", "transformer-engine")


def is_transformer_engine_mxfp8_available():
    if _is_package_available("transformer_engine", "transformer-engine"):
        import transformer_engine.pytorch as te

        return te.fp8.check_mxfp8_support()[0]
    return False


def is_lomo_available():
    return _is_package_available("lomo_optim")


def is_cuda_available():
    """
    Checks if `cuda` is available via an `nvml-based` check which won't trigger the drivers and leave cuda
    uninitialized.
    """
    with patch_environment(PYTORCH_NVML_BASED_CUDA_CHECK="1"):
        available = torch.cuda.is_available()

    return available


@lru_cache
def is_torch_xla_available(check_is_tpu=False, check_is_gpu=False):
    """
    Check if `torch_xla` is available. To train a native pytorch job in an environment with torch xla installed, set
    the USE_TORCH_XLA to false.
    """
    assert not (check_is_tpu and check_is_gpu), "The check_is_tpu and check_is_gpu cannot both be true."

    if not _torch_xla_available:
        return False
    elif check_is_gpu:
        return torch_xla.runtime.device_type() in ["GPU", "CUDA"]
    elif check_is_tpu:
        return torch_xla.runtime.device_type() == "TPU"

    return True


def is_torchao_available():
    package_exists = _is_package_available("torchao")
    if package_exists:
        torchao_version = version.parse(importlib.metadata.version("torchao"))
        return compare_versions(torchao_version, ">=", "0.6.1")
    return False


def is_deepspeed_available():
    return _is_package_available("deepspeed")


def is_pippy_available():
    return is_torch_version(">=", "2.4.0")


def is_bf16_available(ignore_tpu=False):
    "Checks if bf16 is supported, optionally ignoring the TPU"
    if is_torch_xla_available(check_is_tpu=True):
        return not ignore_tpu
    if is_cuda_available():
        return torch.cuda.is_bf16_supported()
    if is_mlu_available():
        return torch.mlu.is_bf16_supported()
    if is_xpu_available():
        return torch.xpu.is_bf16_supported()
    if is_mps_available():
        return torch.backends.mps.is_macos_or_newer(14, 0)
    return True


def is_fp16_available():
    "Checks if fp16 is supported"
    if is_habana_gaudi1():
        return False

    return True


def is_fp8_available():
    "Checks if fp8 is supported"
    return is_msamp_available() or is_transformer_engine_available() or is_torchao_available()


def is_4bit_bnb_available():
    package_exists = _is_package_available("bitsandbytes")
    if package_exists:
        bnb_version = version.parse(importlib.metadata.version("bitsandbytes"))
        return compare_versions(bnb_version, ">=", "0.39.0")
    return False


def is_8bit_bnb_available():
    package_exists = _is_package_available("bitsandbytes")
    if package_exists:
        bnb_version = version.parse(importlib.metadata.version("bitsandbytes"))
        return compare_versions(bnb_version, ">=", "0.37.2")
    return False


def is_bnb_available(min_version=None):
    package_exists = _is_package_available("bitsandbytes")
    if package_exists and min_version is not None:
        bnb_version = version.parse(importlib.metadata.version("bitsandbytes"))
        return compare_versions(bnb_version, ">=", min_version)
    else:
        return package_exists


def is_bitsandbytes_multi_backend_available():
    if not is_bnb_available():
        return False
    import bitsandbytes as bnb

    return "multi_backend" in getattr(bnb, "features", set())


def is_torchvision_available():
    return _is_package_available("torchvision")


def is_megatron_lm_available():
    if str_to_bool(os.environ.get("ACCELERATE_USE_MEGATRON_LM", "False")) == 1:
        if importlib.util.find_spec("megatron") is not None:
            try:
                megatron_version = parse(importlib.metadata.version("megatron-core"))
                if compare_versions(megatron_version, ">=", "0.8.0"):
                    return importlib.util.find_spec(".training", "megatron")
            except Exception as e:
                warnings.warn(f"Parse Megatron version failed. Exception:{e}")
                return False


def is_transformers_available():
    return _is_package_available("transformers")


def is_datasets_available():
    return _is_package_available("datasets")


def is_peft_available():
    return _is_package_available("peft")


def is_timm_available():
    return _is_package_available("timm")


def is_triton_available():
    if is_xpu_available():
        return _is_package_available("triton", "pytorch-triton-xpu")
    return _is_package_available("triton")


def is_aim_available():
    package_exists = _is_package_available("aim")
    if package_exists:
        aim_version = version.parse(importlib.metadata.version("aim"))
        return compare_versions(aim_version, "<", "4.0.0")
    return False


def is_tensorboard_available():
    return _is_package_available("tensorboard") or _is_package_available("tensorboardX")


def is_wandb_available():
    return _is_package_available("wandb")


def is_comet_ml_available():
    return _is_package_available("comet_ml")


def is_swanlab_available():
    return _is_package_available("swanlab")


def is_trackio_available():
    return sys.version_info >= (3, 10) and _is_package_available("trackio")


def is_boto3_available():
    return _is_package_available("boto3")


def is_rich_available():
    if _is_package_available("rich"):
        return parse_flag_from_env("ACCELERATE_ENABLE_RICH", False)
    return False


def is_sagemaker_available():
    return _is_package_available("sagemaker")


def is_tqdm_available():
    return _is_package_available("tqdm")


def is_clearml_available():
    return _is_package_available("clearml")


def is_pandas_available():
    return _is_package_available("pandas")


def is_matplotlib_available():
    return _is_package_available("matplotlib")


def is_mlflow_available():
    if _is_package_available("mlflow"):
        return True

    if importlib.util.find_spec("mlflow") is not None:
        try:
            _ = importlib.metadata.metadata("mlflow-skinny")
            return True
        except importlib.metadata.PackageNotFoundError:
            return False
    return False


def is_mps_available(min_version="1.12"):
    "Checks if MPS device is available. The minimum version required is 1.12."
    # With torch 1.12, you can use torch.backends.mps
    # With torch 2.0.0, you can use torch.mps
    return is_torch_version(">=", min_version) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def is_ipex_available():
    "Checks if ipex is installed."

    def get_major_and_minor_from_version(full_version):
        return str(version.parse(full_version).major) + "." + str(version.parse(full_version).minor)

    _torch_version = importlib.metadata.version("torch")
    if importlib.util.find_spec("intel_extension_for_pytorch") is None:
        return False
    _ipex_version = "N/A"
    try:
        _ipex_version = importlib.metadata.version("intel_extension_for_pytorch")
    except importlib.metadata.PackageNotFoundError:
        return False
    torch_major_and_minor = get_major_and_minor_from_version(_torch_version)
    ipex_major_and_minor = get_major_and_minor_from_version(_ipex_version)
    if torch_major_and_minor != ipex_major_and_minor:
        warnings.warn(
            f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
            f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."
        )
        return False
    return True


@lru_cache
def is_mlu_available(check_device=False):
    """
    Checks if `mlu` is available via an `cndev-based` check which won't trigger the drivers and leave mlu
    uninitialized.
    """
    if importlib.util.find_spec("torch_mlu") is None:
        return False

    import torch_mlu  # noqa: F401

    with patch_environment(PYTORCH_CNDEV_BASED_MLU_CHECK="1"):
        available = torch.mlu.is_available()

    return available


@lru_cache
def is_musa_available(check_device=False):
    "Checks if `torch_musa` is installed and potentially if a MUSA is in the environment"
    if importlib.util.find_spec("torch_musa") is None:
        return False

    import torch_musa  # noqa: F401

    if check_device:
        try:
            # Will raise a RuntimeError if no MUSA is found
            _ = torch.musa.device_count()
            return torch.musa.is_available()
        except RuntimeError:
            return False
    return hasattr(torch, "musa") and torch.musa.is_available()


@lru_cache
def is_npu_available(check_device=False):
    "Checks if `torch_npu` is installed and potentially if a NPU is in the environment"
    if importlib.util.find_spec("torch_npu") is None:
        return False

    # NOTE: importing torch_npu may raise error in some envs
    # e.g. inside cpu-only container with torch_npu installed
    try:
        import torch_npu  # noqa: F401
    except Exception:
        return False

    if check_device:
        try:
            # Will raise a RuntimeError if no NPU is found
            _ = torch.npu.device_count()
            return torch.npu.is_available()
        except RuntimeError:
            return False
    return hasattr(torch, "npu") and torch.npu.is_available()


@lru_cache
def is_sdaa_available(check_device=False):
    "Checks if `torch_sdaa` is installed and potentially if a SDAA is in the environment"
    if importlib.util.find_spec("torch_sdaa") is None:
        return False

    import torch_sdaa  # noqa: F401

    if check_device:
        try:
            # Will raise a RuntimeError if no NPU is found
            _ = torch.sdaa.device_count()
            return torch.sdaa.is_available()
        except RuntimeError:
            return False
    return hasattr(torch, "sdaa") and torch.sdaa.is_available()


@lru_cache
def is_hpu_available(init_hccl=False):
    "Checks if `torch.hpu` is installed and potentially if a HPU is in the environment"
    if (
        importlib.util.find_spec("habana_frameworks") is None
        or importlib.util.find_spec("habana_frameworks.torch") is None
    ):
        return False

    import habana_frameworks.torch  # noqa: F401

    if init_hccl:
        import habana_frameworks.torch.distributed.hccl as hccl  # noqa: F401

    return hasattr(torch, "hpu") and torch.hpu.is_available()


def is_habana_gaudi1():
    if is_hpu_available():
        import habana_frameworks.torch.utils.experimental as htexp  # noqa: F401

        if htexp._get_device_type() == htexp.synDeviceType.synDeviceGaudi:
            return True

    return False


@lru_cache
def is_xpu_available(check_device=False):
    """
    Checks if XPU acceleration is available either via `intel_extension_for_pytorch` or via stock PyTorch (>=2.4) and
    potentially if a XPU is in the environment
    """

    if is_ipex_available():
        import intel_extension_for_pytorch  # noqa: F401
    else:
        if is_torch_version("<=", "2.3"):
            return False

    if check_device:
        try:
            # Will raise a RuntimeError if no XPU  is found
            _ = torch.xpu.device_count()
            return torch.xpu.is_available()
        except RuntimeError:
            return False
    return hasattr(torch, "xpu") and torch.xpu.is_available()


def is_dvclive_available():
    return _is_package_available("dvclive")


def is_torchdata_available():
    return _is_package_available("torchdata")


# TODO: Remove this function once stateful_dataloader is a stable feature in torchdata.
def is_torchdata_stateful_dataloader_available():
    package_exists = _is_package_available("torchdata")
    if package_exists:
        torchdata_version = version.parse(importlib.metadata.version("torchdata"))
        return compare_versions(torchdata_version, ">=", "0.8.0")
    return False


def torchao_required(func):
    """
    A decorator that ensures the decorated function is only called when torchao is available.
    """

    @wraps(func)
    def wrapper(*args, **kwargs):
        if not is_torchao_available():
            raise ImportError(
                "`torchao` is not available, please install it before calling this function via `pip install torchao`."
            )
        return func(*args, **kwargs)

    return wrapper


# TODO: Rework this into `utils.deepspeed` and migrate the "core" chunks into `accelerate.deepspeed`
def deepspeed_required(func):
    """
    A decorator that ensures the decorated function is only called when deepspeed is enabled.
    """

    @wraps(func)
    def wrapper(*args, **kwargs):
        from accelerate.state import AcceleratorState
        from accelerate.utils.dataclasses import DistributedType

        if AcceleratorState._shared_state != {} and AcceleratorState().distributed_type != DistributedType.DEEPSPEED:
            raise ValueError(
                "DeepSpeed is not enabled, please make sure that an `Accelerator` is configured for `deepspeed` "
                "before calling this function."
            )
        return func(*args, **kwargs)

    return wrapper


def is_weights_only_available():
    # Weights only with allowlist was added in 2.4.0
    # ref: https://github.com/pytorch/pytorch/pull/124331
    return is_torch_version(">=", "2.4.0")


def is_numpy_available(min_version="1.25.0"):
    numpy_version = parse(importlib.metadata.version("numpy"))
    return compare_versions(numpy_version, ">=", min_version)
